Artificial Intelligence Nanodegree¶

Convolutional Neural Networks¶

Project: Write an Algorithm for a Dog Identification App¶


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.

The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.


Why We're Here¶

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

Sample Dog Output

In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!

The Road Ahead¶

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.

  • Step 0: Import Datasets
  • Step 1: Detect Humans
  • Step 2: Detect Dogs
  • Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
  • Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
  • Step 6: Write your Algorithm
  • Step 7: Test Your Algorithm

Step 0: Import Datasets¶

Import Dog Dataset¶

In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:

  • train_files, valid_files, test_files - numpy arrays containing file paths to images
  • train_targets, valid_targets, test_targets - numpy arrays containing onehot-encoded classification labels
  • dog_names - list of string-valued dog breed names for translating labels
In [34]:
from sklearn.datasets import load_files       
from keras.utils import np_utils
import numpy as np
from glob import glob

# define function to load train, test, and validation datasets
def load_dataset(path):
    data = load_files(path)
    dog_files = np.array(data['filenames'])
    dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
    return dog_files, dog_targets

# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')

# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]

# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
There are 133 total dog categories.
There are 8351 total dog images.

There are 6680 training dog images.
There are 835 validation dog images.
There are 836 test dog images.

Import Human Dataset¶

In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.

In [36]:
import random
random.seed(8675309)

# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)

# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.

Step 1: Detect Humans¶

We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.

In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.

In [37]:
import cv2                
import matplotlib.pyplot as plt                        
%matplotlib inline                               

# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')

# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# find faces in image
faces = face_cascade.detectMultiScale(gray)

# print number of faces detected in the image
print('Number of faces detected:', len(faces))

# get bounding box for each detected face
for (x,y,w,h) in faces:
    # add bounding box to color image
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1

Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.

In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.

Write a Human Face Detector¶

We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.

In [4]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) 

(IMPLEMENTATION) Assess the Human Face Detector¶

Question 1: Use the code cell below to test the performance of the face_detector function.

  • What percentage of the first 100 images in human_files have a detected human face?
  • What percentage of the first 100 images in dog_files have a detected human face?

Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.

Answer:

In [5]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
humancounter = 0
dogcounter = 0
for i in range(0,100):
    if face_detector(human_files_short[i]):
        humancounter += 1
    if face_detector(dog_files_short[i]):
        dogcounter += 1
print ('Accuracy for human face detection is {} %'.format(humancounter))
print ('Dog faces detected as human face are {} %'.format(dogcounter))
## TODO: Test the performance of the face_detector algorithm 
## on the images in human_files_short and dog_files_short.
Accuracy for human face detection is 98 %
Dog faces detected as human face are 11 %

Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?

We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.

Answer: In our application, our users will detect the closest matching dog breed when a given face is given. Our model will best be able to capture facial features when the users input their complete and clear face. We will achieve our best results. Hence, Haar cascades face detection model is suitable in our application.


Step 2: Detect Dogs¶

In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.

In [17]:
from keras.applications.resnet50 import ResNet50

# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')

Pre-process the Data¶

When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape

$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$

where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.

The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape

$$ (1, 224, 224, 3). $$

The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape

$$ (\text{nb_samples}, 224, 224, 3). $$

Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!

In [18]:
from keras.preprocessing import image                  
from tqdm import tqdm

def path_to_tensor(img_path):
    # loads RGB image as PIL.Image.Image type
    img = image.load_img(img_path, target_size=(224, 224))
    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
    x = image.img_to_array(img)
    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
    return np.expand_dims(x, axis=0)

def paths_to_tensor(img_paths):
    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
    return np.vstack(list_of_tensors)

Making Predictions with ResNet-50¶

Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.

Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.

By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.

In [19]:
from keras.applications.resnet50 import preprocess_input, decode_predictions

def ResNet50_predict_labels(img_path):
    # returns prediction vector for image located at img_path
    img = preprocess_input(path_to_tensor(img_path))
    return np.argmax(ResNet50_model.predict(img))

Write a Dog Detector¶

While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).

We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).

In [9]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 

(IMPLEMENTATION) Assess the Dog Detector¶

Question 3: Use the code cell below to test the performance of your dog_detector function.

  • What percentage of the images in human_files_short have a detected dog?
  • What percentage of the images in dog_files_short have a detected dog?

Answer:

In [10]:
human_as_dog = 0
dog_as_dog = 0
for i in range(0,len(human_files_short)):
    if dog_detector(human_files_short[i]):
        human_as_dog += 1

        
for i in range(0,len(dog_files_short)):
    if dog_detector(dog_files_short[i]):
        dog_as_dog += 1
                
#To calculate accuracy of our ResNet50 Model            
accuracy_human = human_as_dog/len(human_files_short)*100
accuracy_dog = dog_as_dog/len(dog_files_short)*100

print ('Accuracy for dog detection is {} %'.format(accuracy_dog))
print ('Human faces detected as dog face are {} %'.format(accuracy_human))
Accuracy for dog detection is 100.0 %
Human faces detected as dog face are 1.0 %

Step 3: Create a CNN to Classify Dog Breeds (from Scratch)¶

Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.

Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.

We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.

Brittany Welsh Springer Spaniel

It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).

Curly-Coated Retriever American Water Spaniel

Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.

Yellow Labrador Chocolate Labrador Black Labrador

We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.

Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!

Pre-process the Data¶

We rescale the images by dividing every pixel in every image by 255.

In [11]:
from PIL import ImageFile                            
ImageFile.LOAD_TRUNCATED_IMAGES = True
from keras.preprocessing.image import ImageDataGenerator


# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255

#Augmentating to our training dataset. We add scaling translation and rotational features
datagen = ImageDataGenerator(
    featurewise_center=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,shear_range=0.2,
    zoom_range=0.2)
datagen.fit(train_tensors)
100%|██████████| 6680/6680 [03:25<00:00, 14.44it/s]
100%|██████████| 835/835 [00:23<00:00, 41.46it/s]
100%|██████████| 836/836 [00:23<00:00, 35.25it/s]

(IMPLEMENTATION) Model Architecture¶

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    model.summary()

We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:

Sample CNN

Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.

Answer:

This specific architecture has been chosen after experimenting with a lot of different architectures. From the given model, this architecture has greater depth so that, we can detect more intricate features easily. Pooling is used after each layer, to increase dimensionality and reduce the cross sectional size. We have used kernel initialisation, dropout and Batch normalisation to avoid over-fitting and and make sure, that we can effectively train model infew epochs with proper initialisation. ReLu activation has been used to avoid the vanishing gradient problem. Hence, this architecture is used.

In [13]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense, BatchNormalization
from keras.models import Sequential
from keras import regularizers
model = Sequential()
model.add(Conv2D(filters=20,strides=(1,1),padding='same',kernel_size=2,activation='relu', kernel_regularizer=regularizers.l2(0.01),kernel_initializer='he_normal',input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Conv2D(filters=40,strides=(1,1),kernel_size=2,padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(BatchNormalization())
model.add(Dropout(0.3))

model.add(Conv2D(filters=80,strides=(1,1),padding='same',kernel_size=2,kernel_regularizer=regularizers.l2(0.01),kernel_initializer='he_normal', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(BatchNormalization())
model.add(Dropout(0.3))

model.add(Conv2D(filters=160,strides=(1,1),padding='same',kernel_size=2,kernel_regularizer=regularizers.l2(0.01),kernel_initializer='he_normal', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
model.add(BatchNormalization())
model.add(Dropout(0.3))

model.add(Conv2D(filters=320,strides=(1,1),padding='same',kernel_size=2,kernel_regularizer=regularizers.l2(0.01),kernel_initializer='he_normal', activation='relu'))
model.add(GlobalAveragePooling2D())

model.add(Dense(1024,activation='relu',kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())

model.add(Dense(2048,activation='relu',kernel_initializer='he_normal'))
model.add(Dropout(0.3))
model.add(BatchNormalization())

model.add(Dense(133,activation='softmax'))

### TODO: Define your architecture.

model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_2 (Conv2D)            (None, 224, 224, 20)      260       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 112, 112, 20)      0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 112, 112, 20)      80        
_________________________________________________________________
dropout_1 (Dropout)          (None, 112, 112, 20)      0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 112, 112, 40)      3240      
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 56, 56, 40)        0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 56, 56, 40)        160       
_________________________________________________________________
dropout_2 (Dropout)          (None, 56, 56, 40)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 56, 56, 80)        12880     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 28, 28, 80)        0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 28, 28, 80)        320       
_________________________________________________________________
dropout_3 (Dropout)          (None, 28, 28, 80)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 28, 28, 160)       51360     
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 14, 14, 160)       0         
_________________________________________________________________
batch_normalization_4 (Batch (None, 14, 14, 160)       640       
_________________________________________________________________
dropout_4 (Dropout)          (None, 14, 14, 160)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 14, 14, 320)       205120    
_________________________________________________________________
global_average_pooling2d_1 ( (None, 320)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              328704    
_________________________________________________________________
dropout_5 (Dropout)          (None, 1024)              0         
_________________________________________________________________
batch_normalization_5 (Batch (None, 1024)              4096      
_________________________________________________________________
dense_2 (Dense)              (None, 2048)              2099200   
_________________________________________________________________
dropout_6 (Dropout)          (None, 2048)              0         
_________________________________________________________________
batch_normalization_6 (Batch (None, 2048)              8192      
_________________________________________________________________
dense_3 (Dense)              (None, 133)               272517    
=================================================================
Total params: 2,986,769.0
Trainable params: 2,980,025.0
Non-trainable params: 6,744.0
_________________________________________________________________

Compile the Model¶

In [14]:
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model¶

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [15]:
from keras.callbacks import ModelCheckpoint  

### TODO: specify the number of epochs that you would like to use to train the model.

epochs = 100

### Do NOT modify the code below this line.

checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', 
                               verbose=1, save_best_only=True)

model.fit(train_tensors, train_targets, 
          validation_data=(valid_tensors, valid_targets),
          epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/100
6660/6680 [============================>.] - ETA: 3s - loss: 11.0464 - acc: 0.0120       Epoch 00000: val_loss improved from inf to 11.59581, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1064s - loss: 11.0388 - acc: 0.0120 - val_loss: 11.5958 - val_acc: 0.0096
Epoch 2/100
6660/6680 [============================>.] - ETA: 3s - loss: 6.9119 - acc: 0.0185   Epoch 00001: val_loss improved from 11.59581 to 6.17855, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1328s - loss: 6.9099 - acc: 0.0187 - val_loss: 6.1785 - val_acc: 0.0084
Epoch 3/100
6660/6680 [============================>.] - ETA: 3s - loss: 5.9582 - acc: 0.0176       Epoch 00002: val_loss improved from 6.17855 to 5.44323, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1206s - loss: 5.9591 - acc: 0.0175 - val_loss: 5.4432 - val_acc: 0.0168
Epoch 4/100
6660/6680 [============================>.] - ETA: 3s - loss: 5.6740 - acc: 0.0224   Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 1259s - loss: 5.6752 - acc: 0.0223 - val_loss: 5.9845 - val_acc: 0.0156
Epoch 5/100
6660/6680 [============================>.] - ETA: 3s - loss: 5.4673 - acc: 0.0284       Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 1242s - loss: 5.4684 - acc: 0.0284 - val_loss: 5.5748 - val_acc: 0.0168
Epoch 6/100
6660/6680 [============================>.] - ETA: 3s - loss: 5.3068 - acc: 0.0315       Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 1212s - loss: 5.3052 - acc: 0.0314 - val_loss: 5.5598 - val_acc: 0.0156
Epoch 7/100
6660/6680 [============================>.] - ETA: 3s - loss: 5.2047 - acc: 0.0318   Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 1249s - loss: 5.2040 - acc: 0.0319 - val_loss: 5.9830 - val_acc: 0.0120
Epoch 8/100
6660/6680 [============================>.] - ETA: 3s - loss: 5.0602 - acc: 0.0369       Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 1204s - loss: 5.0599 - acc: 0.0370 - val_loss: 5.9082 - val_acc: 0.0168
Epoch 9/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.9540 - acc: 0.0438   Epoch 00008: val_loss improved from 5.44323 to 4.97345, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1203s - loss: 4.9536 - acc: 0.0439 - val_loss: 4.9734 - val_acc: 0.0467
Epoch 10/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.8474 - acc: 0.0464       Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 1190s - loss: 4.8468 - acc: 0.0463 - val_loss: 6.7309 - val_acc: 0.0287
Epoch 11/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.8128 - acc: 0.0461   Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 1194s - loss: 4.8118 - acc: 0.0461 - val_loss: 5.6341 - val_acc: 0.0240
Epoch 12/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.7128 - acc: 0.0511       Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 1198s - loss: 4.7142 - acc: 0.0509 - val_loss: 6.3312 - val_acc: 0.0228
Epoch 13/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.6140 - acc: 0.0553   Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 1200s - loss: 4.6158 - acc: 0.0551 - val_loss: 5.0947 - val_acc: 0.0299
Epoch 14/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.5520 - acc: 0.0595   Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 1198s - loss: 4.5522 - acc: 0.0593 - val_loss: 11.1425 - val_acc: 0.0084
Epoch 15/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.4516 - acc: 0.0637   Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 1198s - loss: 4.4525 - acc: 0.0635 - val_loss: 7.4937 - val_acc: 0.0228
Epoch 16/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.4015 - acc: 0.0707       Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 1193s - loss: 4.4025 - acc: 0.0707 - val_loss: 5.1604 - val_acc: 0.0371
Epoch 17/100
6660/6680 [============================>.] - ETA: 38s - loss: 4.3565 - acc: 0.0764   Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 12826s - loss: 4.3557 - acc: 0.0763 - val_loss: 7.4212 - val_acc: 0.0192
Epoch 18/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.2491 - acc: 0.0844   Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 1227s - loss: 4.2481 - acc: 0.0844 - val_loss: 5.0936 - val_acc: 0.0599
Epoch 19/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.1623 - acc: 0.0952   Epoch 00018: val_loss improved from 4.97345 to 4.84682, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1271s - loss: 4.1613 - acc: 0.0952 - val_loss: 4.8468 - val_acc: 0.0599
Epoch 20/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.1157 - acc: 0.1042   Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 1226s - loss: 4.1164 - acc: 0.1042 - val_loss: 6.2565 - val_acc: 0.0347
Epoch 21/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.1066 - acc: 0.0964   Epoch 00020: val_loss improved from 4.84682 to 4.28235, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1133s - loss: 4.1065 - acc: 0.0966 - val_loss: 4.2823 - val_acc: 0.0838
Epoch 22/100
6660/6680 [============================>.] - ETA: 3s - loss: 4.0325 - acc: 0.1065   Epoch 00021: val_loss did not improve
6680/6680 [==============================] - 1130s - loss: 4.0322 - acc: 0.1064 - val_loss: 6.0670 - val_acc: 0.0323
Epoch 23/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.9758 - acc: 0.1156   Epoch 00022: val_loss did not improve
6680/6680 [==============================] - 1138s - loss: 3.9757 - acc: 0.1156 - val_loss: 8.1460 - val_acc: 0.0311
Epoch 24/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.9240 - acc: 0.1239   Epoch 00023: val_loss did not improve
6680/6680 [==============================] - 1132s - loss: 3.9255 - acc: 0.1235 - val_loss: 4.8402 - val_acc: 0.0563
Epoch 25/100
6660/6680 [============================>.] - ETA: 71s - loss: 3.9088 - acc: 0.1222      Epoch 00024: val_loss did not improve
6680/6680 [==============================] - 23994s - loss: 3.9090 - acc: 0.1223 - val_loss: 4.4849 - val_acc: 0.0862
Epoch 26/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.8564 - acc: 0.1372   Epoch 00025: val_loss did not improve
6680/6680 [==============================] - 1138s - loss: 3.8573 - acc: 0.1371 - val_loss: 5.1506 - val_acc: 0.0826
Epoch 27/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.8407 - acc: 0.1383   Epoch 00026: val_loss did not improve
6680/6680 [==============================] - 1188s - loss: 3.8407 - acc: 0.1382 - val_loss: 4.8547 - val_acc: 0.0731
Epoch 28/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.8145 - acc: 0.1366   Epoch 00027: val_loss did not improve
6680/6680 [==============================] - 1272s - loss: 3.8137 - acc: 0.1364 - val_loss: 4.8981 - val_acc: 0.0683
Epoch 29/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.7949 - acc: 0.1428   Epoch 00028: val_loss did not improve
6680/6680 [==============================] - 1263s - loss: 3.7940 - acc: 0.1428 - val_loss: 6.7592 - val_acc: 0.0587
Epoch 30/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.7487 - acc: 0.1497   Epoch 00029: val_loss did not improve
6680/6680 [==============================] - 1171s - loss: 3.7480 - acc: 0.1497 - val_loss: 4.3821 - val_acc: 0.0802
Epoch 31/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.7230 - acc: 0.1523   Epoch 00030: val_loss did not improve
6680/6680 [==============================] - 1186s - loss: 3.7221 - acc: 0.1524 - val_loss: 9.6551 - val_acc: 0.0431
Epoch 32/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.7013 - acc: 0.1539   Epoch 00031: val_loss did not improve
6680/6680 [==============================] - 1153s - loss: 3.7012 - acc: 0.1537 - val_loss: 7.9303 - val_acc: 0.0383
Epoch 33/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.6875 - acc: 0.1593   Epoch 00032: val_loss did not improve
6680/6680 [==============================] - 1148s - loss: 3.6877 - acc: 0.1588 - val_loss: 5.6780 - val_acc: 0.0790
Epoch 34/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.6803 - acc: 0.1623   Epoch 00033: val_loss did not improve
6680/6680 [==============================] - 1117s - loss: 3.6800 - acc: 0.1623 - val_loss: 4.3233 - val_acc: 0.1341
Epoch 35/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.6503 - acc: 0.1602   Epoch 00034: val_loss did not improve
6680/6680 [==============================] - 1174s - loss: 3.6500 - acc: 0.1602 - val_loss: 6.4437 - val_acc: 0.0659
Epoch 36/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.6406 - acc: 0.1634       Epoch 00035: val_loss did not improve
6680/6680 [==============================] - 1304s - loss: 3.6400 - acc: 0.1632 - val_loss: 5.4041 - val_acc: 0.0683
Epoch 37/100
6660/6680 [============================>.] - ETA: 4s - loss: 3.6459 - acc: 0.1641   Epoch 00036: val_loss did not improve
6680/6680 [==============================] - 1461s - loss: 3.6465 - acc: 0.1638 - val_loss: 6.0251 - val_acc: 0.0491
Epoch 38/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.6147 - acc: 0.1698   Epoch 00037: val_loss did not improve
6680/6680 [==============================] - 1227s - loss: 3.6140 - acc: 0.1698 - val_loss: 4.5963 - val_acc: 0.0838
Epoch 39/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.6041 - acc: 0.1656   Epoch 00038: val_loss did not improve
6680/6680 [==============================] - 1140s - loss: 3.6051 - acc: 0.1656 - val_loss: 5.2511 - val_acc: 0.0695
Epoch 40/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5656 - acc: 0.1733   Epoch 00039: val_loss did not improve
6680/6680 [==============================] - 1129s - loss: 3.5661 - acc: 0.1731 - val_loss: 4.4568 - val_acc: 0.1305
Epoch 41/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5952 - acc: 0.1746   Epoch 00040: val_loss did not improve
6680/6680 [==============================] - 1217s - loss: 3.5948 - acc: 0.1743 - val_loss: 10.4898 - val_acc: 0.0192
Epoch 42/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5585 - acc: 0.1769   Epoch 00041: val_loss did not improve
6680/6680 [==============================] - 1228s - loss: 3.5588 - acc: 0.1768 - val_loss: 4.7398 - val_acc: 0.0719
Epoch 43/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5220 - acc: 0.1890   Epoch 00042: val_loss did not improve
6680/6680 [==============================] - 1284s - loss: 3.5221 - acc: 0.1889 - val_loss: 7.4676 - val_acc: 0.0515
Epoch 44/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5317 - acc: 0.1881   Epoch 00043: val_loss did not improve
6680/6680 [==============================] - 1292s - loss: 3.5330 - acc: 0.1882 - val_loss: 4.9880 - val_acc: 0.0898
Epoch 45/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5043 - acc: 0.1838   Epoch 00044: val_loss did not improve
6680/6680 [==============================] - 1234s - loss: 3.5044 - acc: 0.1840 - val_loss: 7.5042 - val_acc: 0.0551
Epoch 46/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.5161 - acc: 0.1895   Epoch 00045: val_loss did not improve
6680/6680 [==============================] - 1291s - loss: 3.5153 - acc: 0.1897 - val_loss: 5.7145 - val_acc: 0.0886
Epoch 47/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4926 - acc: 0.1844   Epoch 00046: val_loss did not improve
6680/6680 [==============================] - 1323s - loss: 3.4928 - acc: 0.1841 - val_loss: 5.1370 - val_acc: 0.0970
Epoch 48/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4771 - acc: 0.1952   Epoch 00047: val_loss improved from 4.28235 to 4.01350, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1278s - loss: 3.4778 - acc: 0.1948 - val_loss: 4.0135 - val_acc: 0.1557
Epoch 49/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4507 - acc: 0.1974   Epoch 00048: val_loss did not improve
6680/6680 [==============================] - 1248s - loss: 3.4513 - acc: 0.1973 - val_loss: 5.9482 - val_acc: 0.0695
Epoch 50/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4786 - acc: 0.1935   Epoch 00049: val_loss did not improve
6680/6680 [==============================] - 1234s - loss: 3.4782 - acc: 0.1933 - val_loss: 4.4960 - val_acc: 0.0958
Epoch 51/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4880 - acc: 0.1902   Epoch 00050: val_loss did not improve
6680/6680 [==============================] - 1236s - loss: 3.4876 - acc: 0.1907 - val_loss: 7.8662 - val_acc: 0.0539
Epoch 52/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4387 - acc: 0.2075   Epoch 00051: val_loss did not improve
6680/6680 [==============================] - 1252s - loss: 3.4388 - acc: 0.2073 - val_loss: 4.7996 - val_acc: 0.0898
Epoch 53/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4032 - acc: 0.2041   Epoch 00052: val_loss did not improve
6680/6680 [==============================] - 1246s - loss: 3.4017 - acc: 0.2040 - val_loss: 8.0729 - val_acc: 0.0743
Epoch 54/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4163 - acc: 0.2029   Epoch 00053: val_loss did not improve
6680/6680 [==============================] - 1240s - loss: 3.4157 - acc: 0.2031 - val_loss: 4.3344 - val_acc: 0.1461
Epoch 55/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4193 - acc: 0.2045   Epoch 00054: val_loss did not improve
6680/6680 [==============================] - 1225s - loss: 3.4188 - acc: 0.2042 - val_loss: 5.1561 - val_acc: 0.0874
Epoch 56/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.4173 - acc: 0.2078   Epoch 00055: val_loss did not improve
6680/6680 [==============================] - 1214s - loss: 3.4159 - acc: 0.2079 - val_loss: 4.9930 - val_acc: 0.0838
Epoch 57/100
6660/6680 [============================>.] - ETA: 54s - loss: 3.3593 - acc: 0.2128   Epoch 00056: val_loss did not improve
6680/6680 [==============================] - 18233s - loss: 3.3594 - acc: 0.2127 - val_loss: 6.9815 - val_acc: 0.0575
Epoch 58/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3407 - acc: 0.2186   Epoch 00057: val_loss did not improve
6680/6680 [==============================] - 1253s - loss: 3.3396 - acc: 0.2187 - val_loss: 5.1508 - val_acc: 0.0970
Epoch 59/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3570 - acc: 0.2180   Epoch 00058: val_loss did not improve
6680/6680 [==============================] - 1311s - loss: 3.3570 - acc: 0.2177 - val_loss: 9.8091 - val_acc: 0.0287
Epoch 60/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3540 - acc: 0.2123   Epoch 00059: val_loss did not improve
6680/6680 [==============================] - 1277s - loss: 3.3545 - acc: 0.2118 - val_loss: 4.8932 - val_acc: 0.1138
Epoch 61/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3319 - acc: 0.2183   Epoch 00060: val_loss did not improve
6680/6680 [==============================] - 1300s - loss: 3.3331 - acc: 0.2181 - val_loss: 6.6093 - val_acc: 0.0778
Epoch 62/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3672 - acc: 0.2153   Epoch 00061: val_loss did not improve
6680/6680 [==============================] - 1247s - loss: 3.3660 - acc: 0.2159 - val_loss: 4.5220 - val_acc: 0.1174
Epoch 63/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3254 - acc: 0.2231   Epoch 00062: val_loss did not improve
6680/6680 [==============================] - 1069s - loss: 3.3251 - acc: 0.2234 - val_loss: 4.2493 - val_acc: 0.1377
Epoch 64/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3447 - acc: 0.2201   Epoch 00063: val_loss did not improve
6680/6680 [==============================] - 1084s - loss: 3.3448 - acc: 0.2204 - val_loss: 4.8770 - val_acc: 0.1210
Epoch 65/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3334 - acc: 0.2293   Epoch 00064: val_loss did not improve
6680/6680 [==============================] - 1070s - loss: 3.3340 - acc: 0.2293 - val_loss: 4.3033 - val_acc: 0.1413
Epoch 66/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3062 - acc: 0.2275   Epoch 00065: val_loss did not improve
6680/6680 [==============================] - 1070s - loss: 3.3052 - acc: 0.2275 - val_loss: 4.3215 - val_acc: 0.1497
Epoch 67/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2774 - acc: 0.2261   Epoch 00066: val_loss did not improve
6680/6680 [==============================] - 1097s - loss: 3.2797 - acc: 0.2259 - val_loss: 6.9365 - val_acc: 0.0790
Epoch 68/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2646 - acc: 0.2335   Epoch 00067: val_loss did not improve
6680/6680 [==============================] - 1070s - loss: 3.2654 - acc: 0.2332 - val_loss: 4.8898 - val_acc: 0.1281
Epoch 69/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2882 - acc: 0.2354   Epoch 00068: val_loss did not improve
6680/6680 [==============================] - 1073s - loss: 3.2893 - acc: 0.2353 - val_loss: 4.5559 - val_acc: 0.1521
Epoch 70/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2530 - acc: 0.2350   Epoch 00069: val_loss did not improve
6680/6680 [==============================] - 1066s - loss: 3.2543 - acc: 0.2350 - val_loss: 4.8578 - val_acc: 0.1066
Epoch 71/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2907 - acc: 0.2299   Epoch 00070: val_loss did not improve
6680/6680 [==============================] - 1080s - loss: 3.2921 - acc: 0.2293 - val_loss: 6.5265 - val_acc: 0.1030
Epoch 72/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.3197 - acc: 0.2321   Epoch 00071: val_loss did not improve
6680/6680 [==============================] - 1078s - loss: 3.3185 - acc: 0.2325 - val_loss: 4.7989 - val_acc: 0.1281
Epoch 73/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2663 - acc: 0.2315   Epoch 00072: val_loss did not improve
6680/6680 [==============================] - 1099s - loss: 3.2665 - acc: 0.2319 - val_loss: 4.2909 - val_acc: 0.1234
Epoch 74/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2695 - acc: 0.2407   Epoch 00073: val_loss did not improve
6680/6680 [==============================] - 1207s - loss: 3.2686 - acc: 0.2407 - val_loss: 5.1044 - val_acc: 0.1078
Epoch 75/100
6660/6680 [============================>.] - ETA: 4s - loss: 3.2452 - acc: 0.2443   Epoch 00074: val_loss did not improve
6680/6680 [==============================] - 1572s - loss: 3.2461 - acc: 0.2442 - val_loss: 4.2366 - val_acc: 0.1521
Epoch 76/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2105 - acc: 0.2444   Epoch 00075: val_loss did not improve
6680/6680 [==============================] - 1196s - loss: 3.2105 - acc: 0.2446 - val_loss: 4.3741 - val_acc: 0.1401
Epoch 77/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2217 - acc: 0.2497   Epoch 00076: val_loss did not improve
6680/6680 [==============================] - 1190s - loss: 3.2223 - acc: 0.2496 - val_loss: 6.4599 - val_acc: 0.0898
Epoch 78/100
6660/6680 [============================>.] - ETA: 5s - loss: 3.2295 - acc: 0.2458   Epoch 00077: val_loss improved from 4.01350 to 3.94665, saving model to saved_models/weights.best.from_scratch.hdf5
6680/6680 [==============================] - 1863s - loss: 3.2284 - acc: 0.2463 - val_loss: 3.9467 - val_acc: 0.1784
Epoch 79/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2475 - acc: 0.2387   Epoch 00078: val_loss did not improve
6680/6680 [==============================] - 1277s - loss: 3.2465 - acc: 0.2389 - val_loss: 11.2069 - val_acc: 0.0311
Epoch 80/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2128 - acc: 0.2509   Epoch 00079: val_loss did not improve
6680/6680 [==============================] - 1355s - loss: 3.2133 - acc: 0.2507 - val_loss: 8.3321 - val_acc: 0.0371
Epoch 81/100
6660/6680 [============================>.] - ETA: 4s - loss: 3.2352 - acc: 0.2495   Epoch 00080: val_loss did not improve
6680/6680 [==============================] - 1415s - loss: 3.2346 - acc: 0.2494 - val_loss: 5.3941 - val_acc: 0.1138
Epoch 82/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2098 - acc: 0.2589   Epoch 00081: val_loss did not improve
6680/6680 [==============================] - 1265s - loss: 3.2091 - acc: 0.2585 - val_loss: 7.1077 - val_acc: 0.0802
Epoch 83/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2043 - acc: 0.2464   Epoch 00082: val_loss did not improve
6680/6680 [==============================] - 1224s - loss: 3.2022 - acc: 0.2469 - val_loss: 5.0029 - val_acc: 0.1425
Epoch 84/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2128 - acc: 0.2511   Epoch 00083: val_loss did not improve
6680/6680 [==============================] - 1226s - loss: 3.2127 - acc: 0.2510 - val_loss: 4.4710 - val_acc: 0.1377
Epoch 85/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2101 - acc: 0.2557   Epoch 00084: val_loss did not improve
6680/6680 [==============================] - 1216s - loss: 3.2108 - acc: 0.2555 - val_loss: 7.9501 - val_acc: 0.0347
Epoch 86/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2068 - acc: 0.2566   Epoch 00085: val_loss did not improve
6680/6680 [==============================] - 1219s - loss: 3.2068 - acc: 0.2566 - val_loss: 4.3770 - val_acc: 0.1796
Epoch 87/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.2059 - acc: 0.2556   Epoch 00086: val_loss did not improve
6680/6680 [==============================] - 1228s - loss: 3.2058 - acc: 0.2557 - val_loss: 7.1983 - val_acc: 0.0790
Epoch 88/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1945 - acc: 0.2589   Epoch 00087: val_loss did not improve
6680/6680 [==============================] - 1203s - loss: 3.1949 - acc: 0.2584 - val_loss: 4.1271 - val_acc: 0.1760
Epoch 89/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1563 - acc: 0.2662   Epoch 00088: val_loss did not improve
6680/6680 [==============================] - 1198s - loss: 3.1564 - acc: 0.2663 - val_loss: 4.8022 - val_acc: 0.1353
Epoch 90/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1919 - acc: 0.2577   Epoch 00089: val_loss did not improve
6680/6680 [==============================] - 1198s - loss: 3.1926 - acc: 0.2575 - val_loss: 5.2500 - val_acc: 0.1246
Epoch 91/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1384 - acc: 0.2734   Epoch 00090: val_loss did not improve
6680/6680 [==============================] - 1210s - loss: 3.1375 - acc: 0.2740 - val_loss: 4.4173 - val_acc: 0.1497
Epoch 92/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1576 - acc: 0.2611   Epoch 00091: val_loss did not improve
6680/6680 [==============================] - 1218s - loss: 3.1589 - acc: 0.2611 - val_loss: 4.3113 - val_acc: 0.1449
Epoch 93/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1179 - acc: 0.2763   Epoch 00092: val_loss did not improve
6680/6680 [==============================] - 1253s - loss: 3.1180 - acc: 0.2759 - val_loss: 4.9975 - val_acc: 0.1030
Epoch 94/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1942 - acc: 0.2602   Epoch 00093: val_loss did not improve
6680/6680 [==============================] - 1217s - loss: 3.1948 - acc: 0.2603 - val_loss: 6.0990 - val_acc: 0.0922
Epoch 95/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1369 - acc: 0.2739   Epoch 00094: val_loss did not improve
6680/6680 [==============================] - 1245s - loss: 3.1350 - acc: 0.2744 - val_loss: 8.2986 - val_acc: 0.0575
Epoch 96/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1511 - acc: 0.2655   Epoch 00095: val_loss did not improve
6680/6680 [==============================] - 1223s - loss: 3.1535 - acc: 0.2651 - val_loss: 4.6976 - val_acc: 0.1401
Epoch 97/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.0896 - acc: 0.2814   Epoch 00096: val_loss did not improve
6680/6680 [==============================] - 1234s - loss: 3.0906 - acc: 0.2811 - val_loss: 6.9773 - val_acc: 0.0934
Epoch 98/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1033 - acc: 0.2746   Epoch 00097: val_loss did not improve
6680/6680 [==============================] - 1216s - loss: 3.1038 - acc: 0.2750 - val_loss: 4.2590 - val_acc: 0.1581
Epoch 99/100
6660/6680 [============================>.] - ETA: 6s - loss: 3.1084 - acc: 0.2746   Epoch 00098: val_loss did not improve
6680/6680 [==============================] - 2106s - loss: 3.1086 - acc: 0.2744 - val_loss: 4.1580 - val_acc: 0.1856
Epoch 100/100
6660/6680 [============================>.] - ETA: 3s - loss: 3.1289 - acc: 0.2695   Epoch 00099: val_loss did not improve
6680/6680 [==============================] - 1232s - loss: 3.1273 - acc: 0.2696 - val_loss: 4.1730 - val_acc: 0.1796
Out[15]:
<keras.callbacks.History at 0x127aade10>

Load the Model with the Best Validation Loss¶

In [ ]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')

Test the Model¶

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.

In [16]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]

# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 18.1818%

Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)¶

You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.

In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:

The files are encoded as such:

Dog{network}Data.npz

where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.

(IMPLEMENTATION) Obtain Bottleneck Features¶

In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:

bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
In [8]:
### TODO: Obtain bottleneck features from another pre-trained CNN.
import numpy as np

bottleneck_features = np.load('bottleneck_features/DogXceptionData.npz')
train_Xception = bottleneck_features['train']
valid_Xception = bottleneck_features['valid']
test_Xception = bottleneck_features['test']

(IMPLEMENTATION) Model Architecture¶

Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:

    <your model's name>.summary()

Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.

Answer:

To arrive at this architecture, I was considered architectures from all transfer learning models, where I connected the output from each tranfer model directly to a dense layer with softmax activation function to produce classification results. Aamongst these, models Xception provided the maximum accuracy on the test dataset. Hence, an Xception model was chose. Post, this I tried different architectures to build upon the accuracy provided by the Xception model. The chosen architecture then provided with the maximum accuracy and was hence chosen.

In [9]:
### TODO: Define your architecture.
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense, BatchNormalization
from keras.models import Sequential
from keras import regularizers
from keras.callbacks import ModelCheckpoint  

Xception_model=Sequential()
Xception_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:]))
Xception_model.add(Dense(1024,activation='relu',kernel_initializer='he_normal'))
Xception_model.add(Dropout(0.3))
Xception_model.add(BatchNormalization())
Xception_model.add(Dense(2048,activation='relu',kernel_initializer='he_normal'))
Xception_model.add(Dropout(0.3))
Xception_model.add(BatchNormalization())
Xception_model.add(Dense(133, activation='softmax'))
Xception_model.summary()          
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
global_average_pooling2d_2 ( (None, 2048)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 1024)              2098176   
_________________________________________________________________
dropout_3 (Dropout)          (None, 1024)              0         
_________________________________________________________________
batch_normalization_3 (Batch (None, 1024)              4096      
_________________________________________________________________
dense_5 (Dense)              (None, 2048)              2099200   
_________________________________________________________________
dropout_4 (Dropout)          (None, 2048)              0         
_________________________________________________________________
batch_normalization_4 (Batch (None, 2048)              8192      
_________________________________________________________________
dense_6 (Dense)              (None, 133)               272517    
=================================================================
Total params: 4,482,181.0
Trainable params: 4,476,037.0
Non-trainable params: 6,144.0
_________________________________________________________________

(IMPLEMENTATION) Compile the Model¶

In [10]:
### TODO: Compile the model.
Xception_model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

(IMPLEMENTATION) Train the Model¶

Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.

You are welcome to augment the training data, but this is not a requirement.

In [11]:
### TODO: Train the model.


checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Xception.hdf5', 
                               verbose=1, save_best_only=True)

Xception_model.fit(train_Xception, train_targets, 
          validation_data=(valid_Xception, valid_targets),
          epochs=150, batch_size=30, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples
Epoch 1/150
6660/6680 [============================>.] - ETA: 0s - loss: 1.4783 - acc: 0.6485       - ETA: 11s - loss: 1.6989 - acc: 0.6057 - ETA: 1s - loss: 1.4996 - acc: 0.6446Epoch 00000: val_loss improved from inf to 0.68547, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 48s - loss: 1.4765 - acc: 0.6488 - val_loss: 0.6855 - val_acc: 0.8120
Epoch 2/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.5881 - acc: 0.8255  - ETA: 19s - loss: 0.5878 - acc: 0.8257 - ETA: 1s - loss: 0.5875 - acc: 0.8252Epoch 00001: val_loss improved from 0.68547 to 0.54827, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 46s - loss: 0.5877 - acc: 0.8257 - val_loss: 0.5483 - val_acc: 0.8407
Epoch 3/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.4268 - acc: 0.8727  - ETA: 27s - loss: 0.4036 - acc: 0.8750Epoch 00002: val_loss improved from 0.54827 to 0.52377, saving model to saved_models/weights.best.Xception.hdf5
6680/6680 [==============================] - 45s - loss: 0.4267 - acc: 0.8728 - val_loss: 0.5238 - val_acc: 0.8443
Epoch 4/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.3490 - acc: 0.8895 Epoch 00003: val_loss did not improve
6680/6680 [==============================] - 45s - loss: 0.3491 - acc: 0.8894 - val_loss: 0.5309 - val_acc: 0.8419
Epoch 5/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.2652 - acc: 0.9170 Epoch 00004: val_loss did not improve
6680/6680 [==============================] - 44s - loss: 0.2661 - acc: 0.9171 - val_loss: 0.5568 - val_acc: 0.8359
Epoch 6/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.2340 - acc: 0.9282 Epoch 00005: val_loss did not improve
6680/6680 [==============================] - 44s - loss: 0.2336 - acc: 0.9284 - val_loss: 0.5451 - val_acc: 0.8443
Epoch 7/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.1871 - acc: 0.9411 Epoch 00006: val_loss did not improve
6680/6680 [==============================] - 45s - loss: 0.1867 - acc: 0.9413 - val_loss: 0.5517 - val_acc: 0.8467
Epoch 8/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.1586 - acc: 0.9502 Epoch 00007: val_loss did not improve
6680/6680 [==============================] - 45s - loss: 0.1591 - acc: 0.9500 - val_loss: 0.6174 - val_acc: 0.8419
Epoch 9/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.1348 - acc: 0.9620 Epoch 00008: val_loss did not improve
6680/6680 [==============================] - 45s - loss: 0.1349 - acc: 0.9620 - val_loss: 0.6153 - val_acc: 0.8467
Epoch 10/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.1228 - acc: 0.9607  - ETA: 33s - loss: 0.1270 - acc: 0.9628 - ETA: 12s - loss: 0.1247 - acc: 0.9602Epoch 00009: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.1227 - acc: 0.9606 - val_loss: 0.6025 - val_acc: 0.8419
Epoch 11/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0972 - acc: 0.9701  - ETA: 18s - loss: 0.0908 - acc: 0.9731Epoch 00010: val_loss did not improve
6680/6680 [==============================] - 45s - loss: 0.0970 - acc: 0.9702 - val_loss: 0.6077 - val_acc: 0.8551
Epoch 12/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0928 - acc: 0.9728  - ETA: 1s - loss: 0.0913 - acc: 0.9735Epoch 00011: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0925 - acc: 0.9729 - val_loss: 0.6209 - val_acc: 0.8551
Epoch 13/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0790 - acc: 0.9775 Epoch 00012: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0789 - acc: 0.9775 - val_loss: 0.6221 - val_acc: 0.8515
Epoch 14/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0628 - acc: 0.9842 Epoch 00013: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0627 - acc: 0.9843 - val_loss: 0.6285 - val_acc: 0.8563
Epoch 15/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0702 - acc: 0.9779  - ETA: 1s - loss: 0.0688 - acc: 0.9785Epoch 00014: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0702 - acc: 0.9780 - val_loss: 0.6588 - val_acc: 0.8455
Epoch 16/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0582 - acc: 0.9824  - ETA: 34s - loss: 0.0541 - acc: 0.9848Epoch 00015: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0583 - acc: 0.9825 - val_loss: 0.7482 - val_acc: 0.8491
Epoch 17/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0520 - acc: 0.9842  - ETA: 36s - loss: 0.0512 - acc: 0.9814Epoch 00016: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0519 - acc: 0.9843 - val_loss: 0.6876 - val_acc: 0.8563
Epoch 18/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0479 - acc: 0.9877 Epoch 00017: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0479 - acc: 0.9877 - val_loss: 0.6726 - val_acc: 0.8587
Epoch 19/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0415 - acc: 0.9884 Epoch 00018: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0417 - acc: 0.9883 - val_loss: 0.7132 - val_acc: 0.8575
Epoch 20/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0443 - acc: 0.9878 Epoch 00019: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0443 - acc: 0.9879 - val_loss: 0.7683 - val_acc: 0.8515
Epoch 21/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0409 - acc: 0.9884 Epoch 00020: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0413 - acc: 0.9883 - val_loss: 0.7304 - val_acc: 0.8551
Epoch 22/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0332 - acc: 0.9914  - ETA: 24s - loss: 0.0322 - acc: 0.9921 - ETA: 13s - loss: 0.0307 - acc: 0.9920Epoch 00021: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0332 - acc: 0.9915 - val_loss: 0.6994 - val_acc: 0.8515
Epoch 23/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0305 - acc: 0.9926  - ETA: 24s - loss: 0.0212 - acc: 0.9960Epoch 00022: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0304 - acc: 0.9927 - val_loss: 0.7159 - val_acc: 0.8515
Epoch 24/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0329 - acc: 0.9916 Epoch 00023: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0329 - acc: 0.9916 - val_loss: 0.7370 - val_acc: 0.8527
Epoch 25/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0340 - acc: 0.9911  - ETA: 40s - loss: 0.0279 - acc: 0.9917Epoch 00024: val_loss did not improve
6680/6680 [==============================] - 49s - loss: 0.0339 - acc: 0.9912 - val_loss: 0.7370 - val_acc: 0.8431
Epoch 26/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0255 - acc: 0.9938 Epoch 00025: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0255 - acc: 0.9939 - val_loss: 0.7590 - val_acc: 0.8479
Epoch 27/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0261 - acc: 0.9938  - ETA: 26s - loss: 0.0200 - acc: 0.9953Epoch 00026: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0260 - acc: 0.9939 - val_loss: 0.7743 - val_acc: 0.8575
Epoch 28/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0245 - acc: 0.9931 Epoch 00027: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0245 - acc: 0.9931 - val_loss: 0.7852 - val_acc: 0.8551
Epoch 29/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0247 - acc: 0.9941 Epoch 00028: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0247 - acc: 0.9942 - val_loss: 0.8069 - val_acc: 0.8455
Epoch 30/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0220 - acc: 0.9949 Epoch 00029: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0220 - acc: 0.9949 - val_loss: 0.7691 - val_acc: 0.8503
Epoch 31/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0215 - acc: 0.9937 Epoch 00030: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0214 - acc: 0.9937 - val_loss: 0.8353 - val_acc: 0.8371
Epoch 32/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0217 - acc: 0.9946 Epoch 00031: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0220 - acc: 0.9945 - val_loss: 0.8198 - val_acc: 0.8503
Epoch 33/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0228 - acc: 0.9938 Epoch 00032: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0230 - acc: 0.9937 - val_loss: 0.7834 - val_acc: 0.8503
Epoch 34/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0214 - acc: 0.9949 Epoch 00033: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0214 - acc: 0.9949 - val_loss: 0.8213 - val_acc: 0.8455
Epoch 35/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0188 - acc: 0.9952  - ETA: 19s - loss: 0.0132 - acc: 0.9966Epoch 00034: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0188 - acc: 0.9952 - val_loss: 0.7997 - val_acc: 0.8539
Epoch 36/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0226 - acc: 0.9938 Epoch 00035: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0226 - acc: 0.9939 - val_loss: 0.8572 - val_acc: 0.8443
Epoch 37/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0148 - acc: 0.9968  - ETA: 24s - loss: 0.0100 - acc: 0.9980Epoch 00036: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0157 - acc: 0.9967 - val_loss: 0.7927 - val_acc: 0.8551
Epoch 38/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0207 - acc: 0.9952 Epoch 00037: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0207 - acc: 0.9952 - val_loss: 0.8545 - val_acc: 0.8491
Epoch 39/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0134 - acc: 0.9968 Epoch 00038: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0134 - acc: 0.9969 - val_loss: 0.8515 - val_acc: 0.8443
Epoch 40/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0174 - acc: 0.9962  - ETA: 6s - loss: 0.0162 - acc: 0.9967Epoch 00039: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0174 - acc: 0.9963 - val_loss: 0.8416 - val_acc: 0.8623
Epoch 41/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0180 - acc: 0.9944 Epoch 00040: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0188 - acc: 0.9943 - val_loss: 0.8506 - val_acc: 0.8515
Epoch 42/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0165 - acc: 0.9959 Epoch 00041: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0165 - acc: 0.9960 - val_loss: 0.8427 - val_acc: 0.8503
Epoch 43/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0143 - acc: 0.9956  - ETA: 30s - loss: 0.0120 - acc: 0.9977 - ETA: 16s - loss: 0.0129 - acc: 0.9967 - ETA: 13s - loss: 0.0133 - acc: 0.9958Epoch 00042: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0143 - acc: 0.9957 - val_loss: 0.8522 - val_acc: 0.8479
Epoch 44/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0177 - acc: 0.9947  - ETA: 30s - loss: 0.0171 - acc: 0.9944Epoch 00043: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0176 - acc: 0.9948 - val_loss: 0.8373 - val_acc: 0.8491
Epoch 45/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0195 - acc: 0.9956  - ETA: 15s - loss: 0.0182 - acc: 0.9961Epoch 00044: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0195 - acc: 0.9957 - val_loss: 0.8562 - val_acc: 0.8539
Epoch 46/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0152 - acc: 0.9959      - ETA: 18s - loss: 0.0173 - acc: 0.9954Epoch 00045: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0151 - acc: 0.9960 - val_loss: 0.8803 - val_acc: 0.8515
Epoch 47/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0138 - acc: 0.9958  - ETA: 26s - loss: 0.0168 - acc: 0.9941Epoch 00046: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0138 - acc: 0.9958 - val_loss: 0.8239 - val_acc: 0.8659
Epoch 48/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0115 - acc: 0.9973      - ETA: 35s - loss: 0.0047 - acc: 1.0000 - ETA: 31s - loss: 0.0045 - acc: 0.9995Epoch 00047: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0115 - acc: 0.9973 - val_loss: 0.8565 - val_acc: 0.8455
Epoch 49/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0157 - acc: 0.9959      - ETA: 35s - loss: 0.0090 - acc: 0.9967Epoch 00048: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0158 - acc: 0.9960 - val_loss: 0.8132 - val_acc: 0.8575
Epoch 50/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0161 - acc: 0.9964  - ETA: 2s - loss: 0.0156 - acc: 0.9967Epoch 00049: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0161 - acc: 0.9964 - val_loss: 0.7994 - val_acc: 0.8575
Epoch 51/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0179 - acc: 0.9955  - ETA: 29s - loss: 0.0235 - acc: 0.9939Epoch 00050: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0179 - acc: 0.9955 - val_loss: 0.8637 - val_acc: 0.8479
Epoch 52/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0150 - acc: 0.9950  - ETA: 2s - loss: 0.0139 - acc: 0.9954Epoch 00051: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0150 - acc: 0.9951 - val_loss: 0.8489 - val_acc: 0.8563
Epoch 53/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0178 - acc: 0.9965 Epoch 00052: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0178 - acc: 0.9966 - val_loss: 0.8933 - val_acc: 0.8539
Epoch 54/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0155 - acc: 0.9959     Epoch 00053: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0154 - acc: 0.9960 - val_loss: 0.8971 - val_acc: 0.8575
Epoch 55/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0136 - acc: 0.9967  - ETA: 21s - loss: 0.0085 - acc: 0.9974 - ETA: 1s - loss: 0.0136 - acc: 0.9968Epoch 00054: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0136 - acc: 0.9967 - val_loss: 0.8279 - val_acc: 0.8623
Epoch 56/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0137 - acc: 0.9964 Epoch 00055: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0137 - acc: 0.9964 - val_loss: 0.9058 - val_acc: 0.8587
Epoch 57/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0135 - acc: 0.9962     Epoch 00056: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0134 - acc: 0.9963 - val_loss: 0.8845 - val_acc: 0.8491
Epoch 58/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9974     Epoch 00057: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0130 - acc: 0.9975 - val_loss: 0.8880 - val_acc: 0.8515
Epoch 59/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0132 - acc: 0.9971  - ETA: 25s - loss: 0.0108 - acc: 0.9982Epoch 00058: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0134 - acc: 0.9970 - val_loss: 0.8564 - val_acc: 0.8575
Epoch 60/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0125 - acc: 0.9970     Epoch 00059: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0124 - acc: 0.9970 - val_loss: 0.9163 - val_acc: 0.8527
Epoch 61/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0117 - acc: 0.9961      - ETA: 32s - loss: 0.0127 - acc: 0.9952Epoch 00060: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0117 - acc: 0.9961 - val_loss: 0.8923 - val_acc: 0.8563
Epoch 62/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0152 - acc: 0.9962  - ETA: 26s - loss: 0.0204 - acc: 0.9956 - ETA: 21s - loss: 0.0180 - acc: 0.9962 - ETA: 1s - loss: 0.0133 - acc: 0.9965Epoch 00061: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0152 - acc: 0.9963 - val_loss: 0.8249 - val_acc: 0.8671
Epoch 63/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.9983  - ETA: 20s - loss: 0.0097 - acc: 0.9983Epoch 00062: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0089 - acc: 0.9984 - val_loss: 0.8491 - val_acc: 0.8575
Epoch 64/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0123 - acc: 0.9973 Epoch 00063: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0122 - acc: 0.9973 - val_loss: 0.9065 - val_acc: 0.8527
Epoch 65/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0163 - acc: 0.9958 Epoch 00064: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0163 - acc: 0.9958 - val_loss: 0.8917 - val_acc: 0.8551
Epoch 66/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9974      - ETA: 27s - loss: 0.0080 - acc: 0.9973Epoch 00065: val_loss did not improve
6680/6680 [==============================] - 49s - loss: 0.0102 - acc: 0.9975 - val_loss: 0.8807 - val_acc: 0.8587
Epoch 67/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9974      - ETA: 31s - loss: 0.0077 - acc: 0.9984Epoch 00066: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0102 - acc: 0.9975 - val_loss: 0.8622 - val_acc: 0.8575
Epoch 68/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0131 - acc: 0.9962 Epoch 00067: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0131 - acc: 0.9963 - val_loss: 0.8976 - val_acc: 0.8587
Epoch 69/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0177 - acc: 0.9965 Epoch 00068: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0176 - acc: 0.9966 - val_loss: 0.9087 - val_acc: 0.8527
Epoch 70/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9959     Epoch 00069: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0094 - acc: 0.9960 - val_loss: 0.9130 - val_acc: 0.8491
Epoch 71/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9970      - ETA: 10s - loss: 0.0091 - acc: 0.9978 - ETA: 6s - loss: 0.0114 - acc: 0.9972Epoch 00070: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0113 - acc: 0.9970 - val_loss: 0.8977 - val_acc: 0.8467
Epoch 72/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0147 - acc: 0.9968      - ETA: 8s - loss: 0.0140 - acc: 0.9971 - ETA: 0s - loss: 0.0149 - acc: 0.9968Epoch 00071: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0146 - acc: 0.9969 - val_loss: 0.9313 - val_acc: 0.8515
Epoch 73/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0127 - acc: 0.9967  - ETA: 20s - loss: 0.0110 - acc: 0.9978Epoch 00072: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0127 - acc: 0.9967 - val_loss: 0.9331 - val_acc: 0.8575
Epoch 74/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0118 - acc: 0.9964  - ETA: 33s - loss: 0.0133 - acc: 0.9962Epoch 00073: val_loss did not improve
6680/6680 [==============================] - 50s - loss: 0.0118 - acc: 0.9964 - val_loss: 0.9661 - val_acc: 0.8467
Epoch 75/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0087 - acc: 0.9979      - ETA: 33s - loss: 0.0042 - acc: 0.9994Epoch 00074: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0087 - acc: 0.9979 - val_loss: 0.9475 - val_acc: 0.8515
Epoch 76/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9970     Epoch 00075: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0114 - acc: 0.9970 - val_loss: 0.9579 - val_acc: 0.8551
Epoch 77/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9977      - ETA: 34s - loss: 0.0124 - acc: 0.9989Epoch 00076: val_loss did not improve
6680/6680 [==============================] - 49s - loss: 0.0102 - acc: 0.9978 - val_loss: 0.9240 - val_acc: 0.8611
Epoch 78/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0085 - acc: 0.9982     Epoch 00077: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0085 - acc: 0.9982 - val_loss: 0.9484 - val_acc: 0.8515
Epoch 79/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0082 - acc: 0.9980      - ETA: 7s - loss: 0.0078 - acc: 0.9978Epoch 00078: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0082 - acc: 0.9981 - val_loss: 0.9548 - val_acc: 0.8527
Epoch 80/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0094 - acc: 0.9980  - ETA: 17s - loss: 0.0057 - acc: 0.9988Epoch 00079: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0094 - acc: 0.9981 - val_loss: 0.9528 - val_acc: 0.8599
Epoch 81/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9971      - ETA: 32s - loss: 0.0065 - acc: 0.9981Epoch 00080: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0098 - acc: 0.9972 - val_loss: 0.9720 - val_acc: 0.8599
Epoch 82/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9971  - ETA: 38s - loss: 0.0162 - acc: 0.9971 - ETA: 24s - loss: 0.0088 - acc: 0.9976Epoch 00081: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0110 - acc: 0.9972 - val_loss: 0.9769 - val_acc: 0.8563
Epoch 83/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0133 - acc: 0.9968  - ETA: 40s - loss: 0.0262 - acc: 0.9958 - ETA: 14s - loss: 0.0109 - acc: 0.9974 - ETA: 7s - loss: 0.0137 - acc: 0.9970Epoch 00082: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0133 - acc: 0.9969 - val_loss: 0.9183 - val_acc: 0.8563
Epoch 84/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9974  - ETA: 41s - loss: 0.0138 - acc: 0.9929Epoch 00083: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0106 - acc: 0.9975 - val_loss: 0.9775 - val_acc: 0.8527
Epoch 85/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0117 - acc: 0.9973      - ETA: 15s - loss: 0.0107 - acc: 0.9973Epoch 00084: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0118 - acc: 0.9973 - val_loss: 0.9969 - val_acc: 0.8515
Epoch 86/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0119 - acc: 0.9970     Epoch 00085: val_loss did not improve
6680/6680 [==============================] - 49s - loss: 0.0119 - acc: 0.9970 - val_loss: 0.9993 - val_acc: 0.8527
Epoch 87/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0080 - acc: 0.9971 Epoch 00086: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0080 - acc: 0.9972 - val_loss: 1.0077 - val_acc: 0.8527
Epoch 88/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0114 - acc: 0.9976  - ETA: 24s - loss: 0.0129 - acc: 0.9979Epoch 00087: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0114 - acc: 0.9976 - val_loss: 0.9840 - val_acc: 0.8479
Epoch 89/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 0.9976     Epoch 00088: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0088 - acc: 0.9976 - val_loss: 0.9984 - val_acc: 0.8491
Epoch 90/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0130 - acc: 0.9971      - ETA: 34s - loss: 0.0138 - acc: 0.9950Epoch 00089: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0130 - acc: 0.9972 - val_loss: 0.9904 - val_acc: 0.8575
Epoch 91/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0059 - acc: 0.9980 Epoch 00090: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0060 - acc: 0.9981 - val_loss: 1.0069 - val_acc: 0.8503
Epoch 92/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0089 - acc: 0.9980      - ETA: 31s - loss: 0.0055 - acc: 0.9978Epoch 00091: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0089 - acc: 0.9981 - val_loss: 0.9990 - val_acc: 0.8491
Epoch 93/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0139 - acc: 0.9970  - ETA: 20s - loss: 0.0090 - acc: 0.9972Epoch 00092: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0138 - acc: 0.9970 - val_loss: 0.9968 - val_acc: 0.8527
Epoch 94/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0085 - acc: 0.9977 Epoch 00093: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0085 - acc: 0.9978 - val_loss: 1.0249 - val_acc: 0.8539
Epoch 95/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0077 - acc: 0.9980      - ETA: 21s - loss: 0.0087 - acc: 0.9983Epoch 00094: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0078 - acc: 0.9981 - val_loss: 1.0082 - val_acc: 0.8515
Epoch 96/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0075 - acc: 0.9980 Epoch 00095: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0075 - acc: 0.9981 - val_loss: 0.9639 - val_acc: 0.8479
Epoch 97/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0101 - acc: 0.9976     Epoch 00096: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0101 - acc: 0.9976 - val_loss: 0.9851 - val_acc: 0.8539
Epoch 98/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0109 - acc: 0.9974      - ETA: 38s - loss: 0.0025 - acc: 0.9986Epoch 00097: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0109 - acc: 0.9975 - val_loss: 0.9874 - val_acc: 0.8563
Epoch 99/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9973 Epoch 00098: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0097 - acc: 0.9973 - val_loss: 1.0144 - val_acc: 0.8587
Epoch 100/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0069 - acc: 0.9979      - ETA: 30s - loss: 0.0074 - acc: 0.9976Epoch 00099: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0070 - acc: 0.9978 - val_loss: 1.0106 - val_acc: 0.8491
Epoch 101/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0068 - acc: 0.9985  - ETA: 6s - loss: 0.0060 - acc: 0.9984Epoch 00100: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0067 - acc: 0.9985 - val_loss: 1.0166 - val_acc: 0.8479
Epoch 102/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.99731     - ETA: 9s - loss: 0.0102 - acc: 0.9971 Epoch 00101: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0107 - acc: 0.9973 - val_loss: 1.0043 - val_acc: 0.8575
Epoch 103/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0109 - acc: 0.9976  - ETA: 18s - loss: 0.0096 - acc: 0.9975 - ETA: 17s - loss: 0.0095 - acc: 0.9975Epoch 00102: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0109 - acc: 0.9976 - val_loss: 1.0215 - val_acc: 0.8587
Epoch 104/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9979     Epoch 00103: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0104 - acc: 0.9979 - val_loss: 1.0187 - val_acc: 0.8515
Epoch 105/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0124 - acc: 0.9970     Epoch 00104: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0124 - acc: 0.9970 - val_loss: 1.0450 - val_acc: 0.8479
Epoch 106/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0107 - acc: 0.9974      - ETA: 29s - loss: 0.0075 - acc: 0.9982 - ETA: 25s - loss: 0.0070 - acc: 0.9982Epoch 00105: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0107 - acc: 0.9975 - val_loss: 1.0134 - val_acc: 0.8515
Epoch 107/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0076 - acc: 0.9979      - ETA: 38s - loss: 0.0047 - acc: 0.9984Epoch 00106: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0076 - acc: 0.9979 - val_loss: 1.0326 - val_acc: 0.8515
Epoch 108/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9974      - ETA: 17s - loss: 0.0065 - acc: 0.9982 - ETA: 17s - loss: 0.0064 - acc: 0.9983Epoch 00107: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0109 - acc: 0.9975 - val_loss: 1.0289 - val_acc: 0.8515
Epoch 109/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0121 - acc: 0.9974 Epoch 00108: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0121 - acc: 0.9975 - val_loss: 1.0588 - val_acc: 0.8539
Epoch 110/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9974  - ETA: 12s - loss: 0.0105 - acc: 0.9973Epoch 00109: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0100 - acc: 0.9975 - val_loss: 1.0347 - val_acc: 0.8431
Epoch 111/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9980     Epoch 00110: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0100 - acc: 0.9981 - val_loss: 0.9984 - val_acc: 0.8551
Epoch 112/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0117 - acc: 0.9971 Epoch 00111: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0117 - acc: 0.9972 - val_loss: 1.0352 - val_acc: 0.8503
Epoch 113/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9970  - ETA: 28s - loss: 0.0084 - acc: 0.9966 - ETA: 12s - loss: 0.0102 - acc: 0.9971Epoch 00112: val_loss did not improve
6680/6680 [==============================] - 48s - loss: 0.0097 - acc: 0.9970 - val_loss: 1.0291 - val_acc: 0.8527
Epoch 114/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0145 - acc: 0.9968  - ETA: 33s - loss: 0.0086 - acc: 0.9981 - ETA: 18s - loss: 0.0151 - acc: 0.9964Epoch 00113: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0144 - acc: 0.9969 - val_loss: 0.9838 - val_acc: 0.8623
Epoch 115/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0116 - acc: 0.9974     Epoch 00114: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0116 - acc: 0.9975 - val_loss: 0.9751 - val_acc: 0.8515
Epoch 116/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0084 - acc: 0.9985      - ETA: 24s - loss: 0.0086 - acc: 0.9983 - ETA: 23s - loss: 0.0081 - acc: 0.9984Epoch 00115: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0084 - acc: 0.9985 - val_loss: 0.9831 - val_acc: 0.8503
Epoch 117/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0093 - acc: 0.9980     Epoch 00116: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0094 - acc: 0.9981 - val_loss: 1.0421 - val_acc: 0.8479
Epoch 118/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0148 - acc: 0.9967      - ETA: 38s - loss: 0.0221 - acc: 0.9975Epoch 00117: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0148 - acc: 0.9967 - val_loss: 0.9999 - val_acc: 0.8515
Epoch 119/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9970      - ETA: 24s - loss: 0.0047 - acc: 0.9990Epoch 00118: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0109 - acc: 0.9970 - val_loss: 1.1028 - val_acc: 0.8395
Epoch 120/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9973     Epoch 00119: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0105 - acc: 0.9973 - val_loss: 1.0526 - val_acc: 0.8503
Epoch 121/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9979  - ETA: 34s - loss: 0.0170 - acc: 0.9962Epoch 00120: val_loss did not improve
6680/6680 [==============================] - 46s - loss: 0.0102 - acc: 0.9979 - val_loss: 1.0170 - val_acc: 0.8503
Epoch 122/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0111 - acc: 0.9970  - ETA: 16s - loss: 0.0081 - acc: 0.9979Epoch 00121: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0111 - acc: 0.9970 - val_loss: 1.0328 - val_acc: 0.8491
Epoch 123/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0078 - acc: 0.9976     Epoch 00122: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0078 - acc: 0.9976 - val_loss: 1.0331 - val_acc: 0.8515
Epoch 124/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0091 - acc: 0.9977      - ETA: 37s - loss: 0.0153 - acc: 0.9967Epoch 00123: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0091 - acc: 0.9978 - val_loss: 1.0794 - val_acc: 0.8479
Epoch 125/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0110 - acc: 0.9971     Epoch 00124: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0110 - acc: 0.9972 - val_loss: 1.0621 - val_acc: 0.8479
Epoch 126/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9970      - ETA: 37s - loss: 0.0049 - acc: 0.9988 - ETA: 28s - loss: 0.0049 - acc: 0.9986 - ETA: 23s - loss: 0.0058 - acc: 0.9981 - ETA: 15s - loss: 0.0082 - acc: 0.9972Epoch 00125: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0101 - acc: 0.9970 - val_loss: 0.9844 - val_acc: 0.8563
Epoch 127/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9980     Epoch 00126: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0102 - acc: 0.9981 - val_loss: 1.0542 - val_acc: 0.8467
Epoch 128/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0080 - acc: 0.9982      - ETA: 28s - loss: 0.0047 - acc: 0.9978 - ETA: 5s - loss: 0.0081 - acc: 0.9981 - ETA: 5s - loss: 0.0081 - acc: 0.9981Epoch 00127: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0084 - acc: 0.9981 - val_loss: 1.0396 - val_acc: 0.8407
Epoch 129/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0098 - acc: 0.9979 Epoch 00128: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0097 - acc: 0.9979 - val_loss: 1.0722 - val_acc: 0.8467
Epoch 130/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0105 - acc: 0.9974     Epoch 00129: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0105 - acc: 0.9975 - val_loss: 1.0735 - val_acc: 0.8407
Epoch 131/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0083 - acc: 0.9977      - ETA: 10s - loss: 0.0086 - acc: 0.9978Epoch 00130: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0083 - acc: 0.9978 - val_loss: 1.0191 - val_acc: 0.8491
Epoch 132/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0088 - acc: 0.9974     Epoch 00131: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0088 - acc: 0.9975 - val_loss: 1.0200 - val_acc: 0.8539
Epoch 133/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9974     Epoch 00132: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0100 - acc: 0.9975 - val_loss: 1.0315 - val_acc: 0.8563
Epoch 134/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9974      - ETA: 29s - loss: 0.0078 - acc: 0.9969Epoch 00133: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0096 - acc: 0.9975 - val_loss: 0.9892 - val_acc: 0.8563
Epoch 135/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0113 - acc: 0.9971      - ETA: 34s - loss: 0.0064 - acc: 0.9985 - ETA: 0s - loss: 0.0113 - acc: 0.9971Epoch 00134: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0113 - acc: 0.9972 - val_loss: 1.0336 - val_acc: 0.8503
Epoch 136/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0128 - acc: 0.9976      - ETA: 26s - loss: 0.0055 - acc: 0.9981Epoch 00135: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0128 - acc: 0.9976 - val_loss: 1.0231 - val_acc: 0.8563
Epoch 137/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0100 - acc: 0.9974  - ETA: 25s - loss: 0.0040 - acc: 0.9982Epoch 00136: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0099 - acc: 0.9975 - val_loss: 1.0688 - val_acc: 0.8491
Epoch 138/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0081 - acc: 0.9980  - ETA: 29s - loss: 0.0087 - acc: 0.9971 - ETA: 28s - loss: 0.0081 - acc: 0.9971 - ETA: 20s - loss: 0.0061 - acc: 0.9978Epoch 00137: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0080 - acc: 0.9981 - val_loss: 0.9943 - val_acc: 0.8575
Epoch 139/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0115 - acc: 0.9968      - ETA: 29s - loss: 0.0051 - acc: 0.9986Epoch 00138: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0115 - acc: 0.9969 - val_loss: 1.0487 - val_acc: 0.8539
Epoch 140/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0079 - acc: 0.9979     Epoch 00139: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0079 - acc: 0.9979 - val_loss: 0.9621 - val_acc: 0.8599
Epoch 141/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0124 - acc: 0.9971      - ETA: 25s - loss: 0.0097 - acc: 0.9971Epoch 00140: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0123 - acc: 0.9972 - val_loss: 1.0189 - val_acc: 0.8539
Epoch 142/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0136 - acc: 0.9970      - ETA: 37s - loss: 0.0011 - acc: 1.0000 - ETA: 13s - loss: 0.0128 - acc: 0.9972Epoch 00141: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0136 - acc: 0.9970 - val_loss: 1.0008 - val_acc: 0.8563
Epoch 143/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0070 - acc: 0.9980     Epoch 00142: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0071 - acc: 0.9981 - val_loss: 0.9935 - val_acc: 0.8587
Epoch 144/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0087 - acc: 0.9985      - ETA: 5s - loss: 0.0095 - acc: 0.9983Epoch 00143: val_loss did not improve
6680/6680 [==============================] - 51s - loss: 0.0087 - acc: 0.9985 - val_loss: 0.9995 - val_acc: 0.8551
Epoch 145/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.9985      - ETA: 16s - loss: 0.0034 - acc: 0.9993Epoch 00144: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0072 - acc: 0.9985 - val_loss: 0.9752 - val_acc: 0.8491
Epoch 146/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.9986     Epoch 00145: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0055 - acc: 0.9987 - val_loss: 0.9883 - val_acc: 0.8575
Epoch 147/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0056 - acc: 0.9985      - ETA: 26s - loss: 0.0038 - acc: 0.9992Epoch 00146: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0056 - acc: 0.9985 - val_loss: 1.0040 - val_acc: 0.8563
Epoch 148/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0108 - acc: 0.9970      - ETA: 38s - loss: 0.0057 - acc: 0.9962Epoch 00147: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0108 - acc: 0.9970 - val_loss: 1.0026 - val_acc: 0.8587
Epoch 149/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0073 - acc: 0.9976      - ETA: 0s - loss: 0.0073 - acc: 0.9976Epoch 00148: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0073 - acc: 0.9976 - val_loss: 1.0036 - val_acc: 0.8611
Epoch 150/150
6660/6680 [============================>.] - ETA: 0s - loss: 0.0103 - acc: 0.9977      - ETA: 38s - loss: 0.0320 - acc: 0.9944 - ETA: 19s - loss: 0.0101 - acc: 0.9981Epoch 00149: val_loss did not improve
6680/6680 [==============================] - 47s - loss: 0.0103 - acc: 0.9978 - val_loss: 0.9823 - val_acc: 0.8575
Out[11]:
<keras.callbacks.History at 0x113c3fef0>

(IMPLEMENTATION) Load the Model with the Best Validation Loss¶

In [6]:
### TODO: Load the model weights with the best validation loss.
Xception_model.load_weights('saved_models/weights.best.Xception.hdf5')

(IMPLEMENTATION) Test the Model¶

Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.

In [12]:
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set

    
Xception_predictions = [np.argmax(Xception_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Xception]
# report test accuracy
test_accuracy = 100*np.sum(np.array(Xception_predictions)==np.argmax(test_targets, axis=1))/len(Xception_predictions)
print('Test accuracy: %.4f%%'% test_accuracy)
    
Test accuracy: 85.1675%

(IMPLEMENTATION) Predict Dog Breed with the Model¶

Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.

Similar to the analogous function in Step 5, your function should have three steps:

  1. Extract the bottleneck features corresponding to the chosen CNN model.
  2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
  3. Use the dog_names array defined in Step 0 of this notebook to return the corresponding breed.

The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function

extract_{network}

where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.

In [13]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.

from extract_bottleneck_features import *
import cv2

def predict_labels(img_path):
    # extract bottleneck features
    bottleneck_feature = extract_Xception(path_to_tensor(img_path))
    # obtain predicted vector
    predicted_vector = Xception_model.predict(bottleneck_feature)
    # return dog breed that is predicted by the model
    breed = dog_names[np.argmax(predicted_vector)]
    return breed

Step 6: Write your Algorithm¶

Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,

  • if a dog is detected in the image, return the predicted breed.
  • if a human is detected in the image, return the resembling dog breed.
  • if neither is detected in the image, provide output that indicates an error.

You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.

Some sample output for our algorithm is provided below, but feel free to design your own user experience!

Sample Human Output

(IMPLEMENTATION) Write your Algorithm¶

In [30]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.  

def dog_detector(img_path):
    prediction = ResNet50_predict_labels(img_path)
    return ((prediction <= 268) & (prediction >= 151)) 
        
def face_detector(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray)
    return len(faces) 

def print_image(img_path,ax):
    img = cv2.imread(img_path)
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    imgplot = ax.imshow(cv_rgb)
    
def print_image_dog(img_path):
    img = cv2.imread(img_path)
    cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    imgplot=plt.imshow(cv_rgb)
    
def print_similar_dog(breed,ax):
    print_image('display_images/{}.jpg'.format(breed),ax)
    
    
def predict_breed(img_path):
    
    dog_detect = dog_detector(img_path)
    face_detect = face_detector(img_path)
    breed = predict_labels(img_path)
        
    if dog_detect:
        print('Hello dear dog,')
        print_image_dog(img_path)
        print("You look like a {} to our model".format(breed))
        
    elif face_detect:
        print('Hello Human,')
        ax1=plt.subplot(1,2,1)
        print_image(img_path,ax1)
        print('If you were lucky enough to be a dog, you would look like a {}'.format(breed))
        ax2=plt.subplot(1,2,2)
        print_similar_dog(breed,ax2)
        
    else: 
        print('Hello alien, you look very similar to a dog,')
        ax1=plt.subplot(1,2,1)
        print_image(img_path,ax1)
        print('Our model guesses you are a {}'.format(breed))
        ax2=plt.subplot(1,2,2)
        print_similar_dog(breed,ax2)

Step 7: Test Your Algorithm¶

In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?

(IMPLEMENTATION) Test Your Algorithm on Sample Images!¶

Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.

Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.

Answer: The output seems better thab expected. We get a good classification success rate of over 85%. We can also see that, for the specified humans, the dog breeds look similar.

Points of improvement, are: Images with multiple faces. When images with multiple faces are used, out classifier can specify multiple breeds. This would be a very good extension to our current project.

Our algorithm can have a feature where the dog face or breed is superimposed on the human face, like a face swap. By performing this, we would exactly come to know how similar is our predicted breed and the human face.

A lot of dog breeds have unique body structures, like a bull dog has short height but a few dogs are tall. Our algorithm can be trained in a way that we can take as input, these different physical features other than face and then give assessment of breed with closest features.

In [23]:
predict_breed("images/Al_Pacino.jpg")
Hello Human,
If you were lucky enough to be a dog, you would look like a Dachshund
In [24]:
predict_breed("images/Aaron_Eckhart.jpg")
Hello Human,
If you were lucky enough to be a dog, you would look like a Dachshund
In [26]:
predict_breed("images/Adam_Rich.jpg")
Hello Human,
If you were lucky enough to be a dog, you would look like a Parson_russell_terrier
In [31]:
predict_breed("images/Curly-coated_retriever_03896.jpg")
Hello dear dog,
You look like a Curly-coated_retriever to our model
In [32]:
predict_breed("images/Brittany_02625.jpg")
Hello dear dog,
You look like a Brittany to our model
In [33]:
predict_breed("images/American_water_spaniel_00648.jpg")
Hello dear dog,
You look like a Curly-coated_retriever to our model